Patent Application
20190154692
This invention relates to compositions and methods for screening, diagnosing, monitoring and treating gastrointestinal (GI) diseases, including colorectal cancer, gastric cancer, liver cancer, and pancreatic cancer.
Gastrointestinal (GI) diseases are complex chronic human disorders. GI diseases include colorectal cancer, gastric cancer, liver cancer, and pancreatic cancer. GI cancers account for a large percentage of cancer mortalities.
Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the third leading cause of cancer death in both men and women in the United States. Diet, environmental, genetic and inflammation factors contribute in the CRC etiology. Colorectal cancer usually develops over a period of 10 to 20 years. A significant progress has been made in the past decade in reducing the CRC incidence and death rates in the United States, largely due to prevention and early detection of colorectal cancer.
About 25,000 new stomach (gastric) cancer cases are reported in the United States annually. Before a stomach cancer develops, pre-cancerous changes often occur in the inner lining, mucosa, of the stomach. These early changes rarely cause symptoms and therefore often go undetected. The overall 5-year survival rate for patients with stomach cancer is 29% as most patients with stomach cancer are diagnosed after the cancer has already spread to other parts of the body. If stomach cancer is diagnosed and treated before it has spread outside the stomach, the 5-year survival rate is 65%. This data supports a high unmet need for developing a molecular test for detecting stomach cancer at early stages while the patient has not developed symptoms and the cancer has not spread outside the stomach.
Liver cancer is the 10th most common cancer and the 5th most common cause of cancer death among men. It is also the 8th most common cause of cancer death among women. The overall 5-year survival rate for patients with liver cancer is 18%. For 43% of people who are diagnosed at an early stage, the 5-year survival rate is 31%.
Pancreatic cancer (PC) is a lethal malignancy with a very high mortality rate. Pancreatic cancer is a group of heterogeneous diseases and includes cancer of the endocrine (islet cell carcinoma, neuroendocrine carcinoma and carcinoma of carcinoid tumors) and exocrine (pancreatic ductal adenocarcinoma and acinar) pancreas. Among these pathologies, pancreatic ductal adenocarcinoma accounts for approximately 90% of all cases. Notably, a significantly better treatment outcome has been reported in cases where a tumor was detected at an early stage.
Table A lists methods currently available for diagnosing pancreatic cancer.
As shown in Table A, detection of pancreatic cancer relies heavily on procedures, notably imaging. Advances in the imaging technology have allowed improved detection of small lesions. However, these advances have also led to increases in false-positive findings, necessitating invasive procedures to make a definitive diagnosis. Given the probability of false-positive findings associated with the CT screening, there is a substantial need for additional test methods to discriminate between benign vs malignant nodules. There are similar challenges in imaging-based screening for other GI malignancies and a high unmet need for highly sensitive and non-invasive diagnostic tests.
More than 2% of adults harbor a pancreatic cyst, a subset of which progresses to invasive lesions with lethal consequences. As the result of the increasing use of imaging technologies in standard medical practice, pancreatic cysts are being identified with an increasing frequency. Management of these cysts is concomitantly becoming a major clinical problem. Cystic lesions occur in more than 20% of patients examined at autopsy, in as many as 19.6% of patients evaluated by MRI, and in as many as 2.6% of patients evaluated by computed tomography. In the vast majority of cases, the cysts are identified as incidental findings in patients undergoing imaging for symptoms unrelated to pancreatic pathology. However, once a cyst is identified, it poses a challenging life-long management problem. Some cyst types are virtually always benign, some are low-grade malignant, and others are precursors to invasive pancreatic ductal adenocarcinomas. The distinction among cyst types is therefore critical for the effective management of patients with pancreatic cysts.
The potential for malignant transformation varies among pancreatic cystic neoplasms (PCN) subtypes. Imaging and a cyst fluid analysis are sometimes used to identify premalignant or malignant cases that should undergo operative resection. Therefore, there is a critical need to develop an efficient and noninvasive liquid biopsy test which can be used to distinguish a patient with a benign, non-premalignant disease from a patient with malignant pancreatic cysts.
A cancer is associated with major changes in biopathways, including upregulation of fucosyltransferases, sialyltransferases, mannosyl (α-1,6-)-glycoprotein β-1,6-N-acetyl-glucosaminyltransferase. Changes in the expression of glycosyltransferases result in altered glycan assembly, which occurs in the endoplasmic reticulum and Golgi. Accordingly, the glycoprotein products of tumor cells carry aberrant carbohydrate structures compared with their normal counterparts. Typical changes include increased levels of fucose and sialic acid, the addition of polylactosamine units and N-acetylglucosamine, and higher-ordered branching of N-linked glycans. O-linked glycans are also affected in cancer, typically carrying incomplete or prematurely truncated structures relative to those found on normal cells. After secretion or proteolytic cleavage, glycosylated molecules and/or their cleavage products can be released into the interstitial space, where they can enter the circulation. (Drake et al. 2010, Clin Chem, 56(2): 223-236)
Tumors produce glycoproteins that carry oligosaccharides with structures that are markedly different from the same protein produced by a normal cell. A single protein can have many glycosylation sites that greatly amplify the signals they generate compared with their protein backbones, thus tumor glycoproteins can serve as cancer biomarkers. The glycosylation machinery appears to be particularly sensitive to malignant transformation; as a result, the saccharide structures that are added to normal cellular proteins change, resulting in neoglycoforms that can be released from the cell through conventional secretory pathways, or as the result of enhanced proteinase activity. (Drake et al. 2010, Clin Chem, 56(2): 223-236)
Carbohydrates and their associated glycoproteins represent a rich, underexplored source of biomarkers. Glycoproteins with complex glycans are membrane bound or secreted. There is a substantial evidence that cancer cells exhibit altered glycans relative to normal cells. The potential of targeting glycoproteins to identify biomarkers was investigated by enriching N-linked glycopeptides from tissues, cells, and plasma and identifying corresponding peptide sequences and proteins by mass spectrometry. A significant overlap was observed between glycoproteins identified in tissues and cells and glycoproteins identified in plasma, leading to the conclusion that extracellular glycoproteins originating from tissues and cells are released into the blood at concentrations that are detectable by mass spectrometry. See U.S. Patent Publication 2007/0099251.
It has been demonstrated that in pancreatic cancer glucose metabolism pathways and glycosylation levels are changing throughout disease progression, specifically on a background of hypoxia. Hypoxia promotes selective pressure on malignant cells that must develop adaptive metabolic responses to reach their energetic and biosynthetic demands. In a mouse model of pancreatic cancer, it was demonstrated that hypoxic areas from pancreatic ductal adenocarcinoma are mainly composed of epithelial cells harboring epithelial-mesenchymal transition features and expressing glycolytic markers, two characteristics associated with tumor aggressiveness. In this model, it has been also shown that hypoxia increases the “glycolytic” switch of pancreatic cancer cells from oxidative phosphorylation to lactate production and demonstrated that increased lactate efflux from hypoxic cancer cells favors the growth of normoxic cancer cells. (Guillaumond et al. 2013, PNAS, 110(10): 3919-3924).
Metabolized glucose and glutamine converge toward a common pathway, termed the hexosamine biosynthetic pathway, which allows O-linked N-acetylglucosamine modifications of proteins. Importantly, it was reported that hypoxia increases transcription of hexosamine biosynthetic pathway genes as well as levels of O-glycosylated proteins and that O-linked N-acetylglucosaminylation of proteins is a process required for hypoxic pancreatic cancer cell survival. Hypoxia-driven metabolic adaptive processes, such as high glycolytic rate and the hexosamine biosynthetic pathway activation, favor hypoxic and normoxic cancer cell survival and correlate with pancreatic cancer aggressiveness. (Guillaumond et al. 2013, PNAS, Mr 5; 110(10): 3919-3924).
In other studies, it was demonstrated that mucins, specifically, MUC1 and MUC4, are differentially glycosylated as the disease progressed from the early stage to metastatic disease. De novo expression of several mucins correlated with increased metastasis, indicating a potentially more invasive tumor phenotype. (Remmers et al. 2013, Clin Cancer Res. April 15: 19(8)).
There remains the need for an accurate and non-invasive test that can be used to detect and monitor a GI cancer.
Provided is a method for screening, monitoring and/or treating a gastrointestinal (GI) cancer patient, wherein the GI cancer is selected from the group consisting of colorectal cancer, gastric cancer, liver cancer, and pancreatic cancer. A sample from the patient is obtained and glycosylated proteins are isolated from the sample. The isolated glycoproteins are then analyzed for the presence of any of biomarkers from Tables 1A, 2A, 3A, 4A, 5A, 6A, 7A, and any combination thereof. The presence of at least some of the biomarkers in the sample being indicative a GI cancer. The isolated glycosylated proteins can be also grouped into a profile of pathways, and matched with at least one profile selected from the group of profiles of Tables 1, 2, 3, 4, 5, 6, 7, 8, and any combination thereof. At least a partial match with at least one profile from Tables 1, 2, 3, 4, 5, 6, 7, 8 being indicative of a GI cancer.
The sample can be selected from the group consisting of a human tissue biopsy or biosample including pancreas biopsy sample, gastrointestinal sample, blood sample, plasma sample, serum sample, circulating tumor cells sample, tear sample, saliva sample, sperm sample, urine sample, fecal sample and hair sample. Blood or plasma samples are particularly preferred.
The sample can be analyzed using one or more techniques selected from the group consisting of chromatography, gas chromatography, liquid chromatography, mass spectrometry, ELISA, antibody linkage, immunoassay, biochip assay, microarray, nanoassay, spectroscopy, a multiplex molecular assay or techniques which utilize a fluorescent, enzyme, radioactive, metallic, biotin, chemiluminescent, bioluminescent molecule assay. The sample can be analyzed using a combination of a detection techniques of nucleic acids and proteins or peptides.
In the further embodiments of the method, any of biomarkers of Tables 1A, 1, 2A, 2, 3A, 3, 4A, 4, 5A, 5, 6A, 6, 7A and 7 are immobilized on a solid support.
The method can be conducted by reacting the patient's sample with at least one anybody or protein chemistry based reagent specific to at least one biomarker and/or glycobiomarker of Tables 1A, 1, 2A, 2, 3A, 3, 4A, 4, 5A, 6A, 6, 7A or 7. In further embodiments, the method can be conducted by reacting the patient's sample with a synthetic compound or probe which react with at least one protein specific to at least one biomarker and/or glycobiomarker of Tables 1A, 1, 2A, 2, 3A, 3, 4A, 4, 5A, 6A, 6, 7A or 7.
Further embodiments in provide a panel comprising a profile of biomarkers selected from the group consisting of Tables 1A, 1, 2A, 2, 3A, 3, 4A, 4, 5A, 6A, 6, 7A, 7 or8, and any combination thereof. Kits comprising the panels are provided as well.
Further embodiments provide a method for detecting or monitoring a disorder of the pancreas, the method comprising obtaining a sample from a patient and testing the sample for at least one or more biomarker and/or glycobiomarker selected from Tables 5A, 5, 6A, 6 and 9. The disorder of the pancreas is selected from the group consisting of acute pancreatitis, chronic pancreatitis, hereditary pancreatitis, pancreatic neoplasm, and pancreatic cancer. The testing can be conducted by reacting the patient's sample with at least one anybody or protein chemistry based reagent specific to at least one biomarker and/or glycobiomarker of Tables 5A, 5, 6A, 6 and 9. The testing is conducted by reacting the patient's sample with at least one protein specific to at least one biomarker and/or glycobiomarker of Tables 5A, 5, 6A, 6 and 9. The testing can be also conducted by reacting the patient's sample with a synthetic compound or probe which react with at least one protein specific to at least one biomarker and/or glycobiomarker of Tables 5A, 5, 6A, 6 and 9.
Further embodiments provide a method for treating a disorder of the pancreas, the method comprising obtaining a sample from a mammal in need of the treatment and testing the sample for at least one or more biomarker and/or glycobiomarker selected from Tables 5A, 5, 6A, 6 and 9.
This invention provides compositions and methods for detection, screening, monitoring and treatment of gastrointestinal (GI) cancers, including colorectal cancer, gastric cancer, liver cancer, and pancreatic cancer.
Provided is a method by which a patient's protein expression profile is obtained by isolating glycosylated proteins from the patient's liquid biopsy sample. Suitable liquid biopsy samples include blood, plasma, serum or urine. The glycosylated proteins in the profile are grouped into pathways and analyzed for deviations from a profile of a healthy individual. A deviation in a number of the glycosylated proteins in at least one pathway is indicative of the patient's GI cancer. This analysis can be used for developing a treatment plan for a patient with a particular emphasis on using drugs suitable for targeting the affected pathways and/or proteins.
A patient's profile of glycosylated proteins can be also obtained to evaluate results of cancer treatment, including a surgery, chemotherapy, radiation and/or immunotherapy. In this embodiment of the method, a patient's profile of glycosylated proteins after the cancer treatment is comparted to the patient's profile of glycosylated proteins before the cancer treatment. A decrease in the number of abnormally glycosylated proteins means that the treatment is beneficial to the patient. No changes or an increase in the number of abnormally glycosylated proteins means that the treatment plan needs to be modified or cancelled.
A patient's profile of glycosylated proteins can be also obtained to monitor the patient for an onset of a GI cancer. Many patients, including patients with a hereditary history of a GI cancer in a family, can benefit from this procedure which monitors the patient's profile of glycosylated proteins and pathways, and detects any changes in the profile over a period of time.
In one embodiment of the present methods, a patient's profile of glycosylated proteins is prepared by obtaining a blood, plasma, or serum sample from the patient. Glycosylated proteins are then isolated from the sample. Mass spectrometry of protein expression profile is performed to identify the glycosylated proteins in the sample. The patient's profile is then compared to a profile of a healthy individual in order to diagnose a GI cancer. This method can be used to diagnose a GI cancer. In alternative, a patient's profile of glycosylated proteins in a blood, plasma, or serum sample is monitored over a period of time by periodically repeating the analysis in order to detect an early onset of GI cancer or to determine if a particular cancer treatment is beneficial to the patient.
In alternative to the mass spectrometry analysis, a patient's profile of glycosylated proteins and affected pathways can be analyzed with a chip which comprises a set of biomarkers of a GI cancer. In further embodiments, the profiling of glycosylated proteins may comprise identifying affected pathways.
Table 1A discloses glycoproteins differentially expressed in plasma of colorectal cancer (CRC) female patients. These glycoproteins can be used for diagnosing, monitoring and treating a CRC patient by the present methods. In these methods, glycoproteins of Table 1A are used as a set of biomarkers indicative of CRC.
Homo sapiens GN = IGFALS PE = 1 SV = 1
Table 1 provides a profile of abnormalities detected in pathways and glycosylated proteins in plasma of colorectal cancer female patients. This profile, including any of protein biomarkers and pathways, can be used for diagnosing, monitoring and treating a CRC patient by the present methods. In these methods, the profile of Table 1 is used as a set of biomarkers indicative of CRC.
Table 2A discloses glycoproteins differentially expressed in plasma of colorectal cancer (CRC) male patients. These glycoproteins can be used for diagnosing, monitoring and treating a CRC patient by the present methods. In these methods, glycoproteins of Table 2A are used as a set of biomarkers indicative of CRC.
Table 2 provides a profile of abnormalities in pathways and glycosylated proteins in a colorectal cancer male patient. This profile, including any of protein biomarkers and pathways, can be used for diagnosing, monitoring and treating a CRC patient by the present methods. In these methods, the profile of Table 2 is used as a set of biomarkers indicative of CRC.
Table 3A discloses glycoproteins differentially expressed in plasma of gastric cancer female patients. These glycoproteins can be used for diagnosing, monitoring and treating a gastric cancer patient by the present methods. In these methods, glycoproteins of Table 3A are used as a set of biomarkers indicative of gastric cancer.
Table 3 provides a profile of abnormalities in pathways and glycosylated proteins in a gastric cancer female patient. This profile, including any of protein biomarkers and pathways, can be used for diagnosing, monitoring and treating a gastric cancer patient by the present methods. In these methods, the profile of Table 3 is used as a set of biomarkers indicative of gastric cancer.
Table 4A discloses glycoproteins differentially expressed in plasma of gastric cancer male patients. These glycoproteins can be used for diagnosing, monitoring and treating a gastric cancer patient by the present methods. In these methods, glycoproteins of Table 4A are used as a set of biomarkers indicative of gastric cancer.
Table 4 provides a profile of abnormalities in pathways and glycosylated proteins in a gastric cancer male patient. This profile, including any of protein biomarkers and pathways, can be used for diagnosing, monitoring and treating a gastric cancer patient by the present methods.
Table 5A discloses glycoproteins differentially expressed in plasma of pancreatic adenocarcinoma female patients. These glycoproteins can be used for diagnosing, monitoring and treating a pancreatic adenocarcinoma patient by the present methods. In these methods, glycoproteins of Table 5A are used as a set of biomarkers indicative of pancreatic adenocarcinoma cancer.
Table 5 provides a profile of abnormalities in pathways and abnormally glycosylated proteins in a pancreatic adenocarcinoma female patient. This profile, including any of protein biomarkers and pathways, can be used for diagnosing, monitoring and treating a pancreatic adenocarcinoma patient by the present methods.
Table 6A discloses glycoproteins differentially expressed in plasma of pancreatic adenocarcinoma male patients. These glycoproteins can be used for diagnosing, monitoring and treating a pancreatic adenocarcinoma patient by the present methods. In these methods, glycoproteins of Table 6A are used as a set of biomarkers indicative of pancreatic adenocarcinoma cancer.
Table 6 provides a profile of abnormalities in pathways and glycosylated proteins in a pancreatic adenocarcinoma male patient. This profile, including any of protein biomarkers and pathways, can be used for diagnosing, monitoring and treating a pancreatic adenocarcinoma patient by the present methods.
Table 7A discloses glycoproteins differentially expressed in plasma of liver cancer patients. These glycoproteins can be used for diagnosing, monitoring and treating a liver cancer patient by the present methods. In these methods, glycoproteins of Table 5A are used as a set of biomarkers indicative of liver cancer.
Table 7 provides a profile of abnormalities in pathways and glycosylated proteins in a liver cancer male patient. This profile, including any of protein biomarkers and pathways, can be used for diagnosing, monitoring and treating a liver cancer patient by the present methods.
As can be appreciated from the data in Tables 1A-7A and Tables 1-7, some of the protein markers associate specifically with a particular type of GI cancer, while other protein markers are common for several different GI cancers. As can be also appreciated from Tables 1A-7A and Tables 1-7, various pathways are affected in each of the cancers.
The term “biological pathway or pathway” is understood broadly and refers to a set of proteins (and other molecules) that act as a network to initiate, alter or terminate a biological process. Examples of biological pathways include metabolic pathways, gene-regulation pathways, and signal transduction pathways. Dozens and even hundreds of different proteins may comprise a pathway. An activation or inhibition of one protein in a pathway may trigger a chain reaction of activities in the pathway. While two different cancer patients may have a mutation in different proteins, the same pathway may be affected in both patients and lead to the same symptoms. Thus, identifying pathways affected in a cancer patient is a technical advantage of the present methods because it allows to more accurately assess the differences which cause symptoms.
According to some embodiments of the present methods, the first step is to identify proteins abnormally present in a blood or plasma sample of a patient in comparison to a healthy control, as shown in Tables 1A, 2A, 3A, 4A, 5A, 6A, and 7A. The second step is to identify pathways which are enriched (show statistically significantly overlap) with the proteins from the protein profile lists, as shown in Tables 1, 2, 3, 4, 5, 6 and 7.
Using the Fisher's exact test, nonrandom associations between two categorical variables (a pathway and a protein profile list) are determined. Each of the protein profile lists is compared to a pathway database by applying Fisher's exact test consecutively to all pathways in the database to compare the given protein profile and each of the pathways in the database. One pathway database of pathway maps that can be used for this analysis is PATHWAY STUDIO® available from Elsevier, Inc. (Nikitin et al. 2003, Bioinformatics, https://www.elsevier.com/solutions/pathway-studio-biological-research).
This approach was used to identify pathways for glycosylated protein biomarkers in Tables 1-7. PATHWAY STUDIO® software was also used to generate
Table 8 provides a list of pathways which can be used as a biomarker in screening, monitoring and/or treating a GI cancer. In Table 8, a pathway that can be used as a biomarker in colorectal cancer, gastric cancer, liver cancer or pancreatic cancer is identified with an XX.
As can be seen from Table 8, some pathways, such as for example, the adherens junction assembly pathway (
Each of the pathways listed in Table 8 (as defined in more detail in
Further embodiments provide diagnostic methods based on any of the biomarkers in Tables 1, 2, 3, 4, 5, 6, 7 and/or 8, as may be further modified based on
At least in some of these methods, a sample is obtained from a patient. This sample may be a human tissue biopsy or biosample including pancreas biopsy sample, gastrointestinal sample, blood sample, plasma sample, serum sample, circulating tumor cells sample, tear sample, saliva sample, sperm sample, urine sample, fecal sample and hair sample or any other human biospecimen. The sample is then screened to obtain a protein profile and to determine whether the protein profile in the sample matches at least partially a profile from any of Tables 1, 2, 3, 4, 5, 6, or 7. The abnormal proteins in the patient's profile are also analyzed to determine which of the pathways are affected, including the pathways shown in Tables 1-8.
Suitable screening methods may include chromatography, gas chromatography, liquid chromatography, mass spectrometry, ELISA, antibody linkage, immunoassay, biochip assay, microarray, nanoassay, spectroscopy, a multiplex molecular assay or techniques which utilize a fluorescent, enzyme, radioactive, metallic, biotin, chemiluminescent, bioluminescent molecule assay. Suitable methods further include a combination of a detection techniques of nucleic acids and proteins or peptides.
In some methods, at least one biomarker and/or glycobiomarker of Tables 1-8 is immobilized on a solid support. In some methods, the testing is conducted by reacting the patient's sample with at least one anybody or protein chemistry based reagent specific to at least one biomarker and/or glycobiomarker of Tables 1-8.
In further embodiments, the testing is conducted by reacting the patient's sample with at least one protein specific to at least one biomarker and/or glycobiomarker of Tables 1-8. In some embodiments, the testing is conducted by reacting the patient's sample with a synthetic compound or probe which reacts with at least one protein specific to at least one biomarker and/or glycobiomarker of Tables 1-8.
Further embodiments include a method for diagnosing, monitoring and treating a GI cancer, the method comprising obtaining a blood or plasma sample from a patient in need of the treatment, analyzing glycoproteins in the sample, creating a profile of pathways and glycoproteins for the patient, and comparing the profile to the profiles of glycobiomarkers and pathways of Tables 1-8.
In some embodiments, a screening can be conducted with a patient's sample without protein extraction. In further embodiments, proteins are isolated from the patient's sample, such as a blood or plasma sample, and a test is conducted with the isolated proteins. In some embodiments, all proteins in the sample are analyzed. In other embodiments, the analysis is conducted only for proteins which are glycosylated. In further embodiments, only GlcNac glycosylated proteins are analyzed.
The advantages of these screening methods include: these tests are non-invasive and they can be conducted in a very short period of time. In some embodiments, the same test can be repeated several times within a period of time to monitor progression of a GI cancer and/or evaluate the efficiency of a treatment plan.
Further embodiments provide Multiplex Molecular Diagnostic Protein assays, a combination of protein assay and assay based on detection of DNA or RNA, and kits, including an immunoassay, biochip assay, nanoassay and molecular assay. In some embodiments, an assay detects protein biomarker and/or glycobiomarkers or peptides derived from the biomarker and/or glycobiomarkers from any of Tables 1-8 and
In further embodiments, a patient's sample is reacted with a set of antibodies, each of which is selectively specific for at least one biomarker and/or glycobiomarker from Tables 1, 2, 3, 4, 5, 6, 7 or 8. The complex between an antibody and a glycobiomarker or a biomarker is then may be detected with a second antibody conjugated to a detection molecule.
Further embodiments include methods for detecting and monitoring a GI cancer. In these methods, a patient's sample is tested for expression of at least some biomarker and/or glycobiomarkers listed in Table 2, 3, 4, 5, 6, 7 or 8.
Further embodiments include methods in which patient's response to therapy, such as for example surgery, radiation, immunotherapy or chemotherapy, is monitored with testing a patient's sample for expression of at least some biomarker and/or glycobiomarkers listed in Tables 1, 2, 3, 4, 5, 6, 7 and 8. Other applications include detecting a recurrent or residual GI cancer by testing a patient's sample for expression of at least some biomarker and/or glycobiomarkers listed in Tables 1, 2, 3, 4, 5, 6 and/or 7.
Other applications include screening of genetically predisposed individuals for a GI caner by testing the individual's sample for expression of at least some biomarkers and/or glycobiomarkers listed in Tables 1, 2, 3, 4, 5, 6 and/or 7. Such genetically predisposed individuals include, but not limited, to BRCA mutation carriers; PALB2 mutation carriers; p16 mutation carriers; Lynch syndrome patients; Peutz-Jeghers syndrome patients; and individuals with a family history of a GI cancer.
In some methods, a biochip comprising a set of at least one or more biomarker and/or glycobiomarkers listed in Tables 1, 2, 3, 4, 5, 6 and/or 7 can be used as a robust and sensitive tool to monitor a GI cancer progression and response to therapy. These biochips can be also used as a biomarker or molecular modality for drug development or drug optimization.
In some embodiments, a patient can be screened and evaluated based on a test conducted with the patient's sample and a panel of biomarker and/or glycobiomarkers and pathways which include at least one or more biomarkers listed in Tables 1, 2, 3, 4, 5, 6, 7 and/or 8.
This invention also provides compositions and methods for selective detection of pancreatic diseases and/or disorders of the pancreas, including pancreatic cancer, pancreatitis, acute pancreatitis, chronic pancreatitis, hereditary pancreatitis, autoimmune pancreatitis, and pancreatic neoplasm. It also provides compositions and methods for monitoring progression of a pancreatic disease and/or disorder of the pancreas, including, but not limited to, pancreatic cancer, pancreatitis, and autoimmune pancreatitis
The invention provides a panel of pancreatic disease biomarkers. These biomarkers may include glycosylated biomarkers. In some embodiments, a panel of biomarkers include at least one or more glycosylated biomarkers listed in Tables 5A, 5, 6A, and 6. In other embodiments, a panel of biomarkers include all biomarkers listed in Tables 5A, 5, 6A, and 6. Further embodiments include a panel which comprises at least one or more biomarkers as listed in Table 9. In further embodiments, a panel includes a combination of at least one or more biomarkers from Table 9 and at least one or more biomarkers from any of the tables 5A, 5, 6A, and 6.
In some embodiments, a panel of biomarkers includes at least one or more proteins listed in Table 9. In other embodiments, a panel of biomarkers includes all proteins listed in Table 9. Further embodiments include a panel which comprises at least one or more glycosylated biomarkers as listed in any of the Tables 5A, 5, 6A, and 6.. In further embodiments, a panel includes a combination of at least one or more biomarkers from Tables 5A, 5, 6A, 6 and 9.
sapiens GN = GAPDH PE = 1 SV = 3
sapiens GN = LAMP1 PE = 1 SV = 3
sapiens GN = CHL1 PE = 1 SV = 4
sapiens GN = PTPRJ PE = 1 SV = 3
Other embodiments provide kits which comprise at least one panel as described above in connection with Tables 5A, 5, 6A, 6 and 9. Such kits may further comprise a biochip.
Various methods are contemplated and may include an immunoassay, biochip assay, nanoassay in which at least one panel with at least one or more biomarkers from
Tables 5A, 5, 6A and 6 or at least one or more biomarkers from Table 9 are used. At least in some of these methods, a sample is obtained from a patient. This sample may be a human tissue biopsy or biosample including pancreas biopsy sample, gastrointestinal sample, blood sample, plasma sample, serum sample, circulating tumor cells sample, tear sample, saliva sample, sperm sample, urine sample, fecal sample and hair sample or any other human biospecimen.
The sample is then screened for presence of at least one or more glycosylated markers listed in Tables 5A, 5, 6A, 6 and/or for presence of at least one or more protein markers listed in Table 9.
Suitable screening methods may include chromatography, gas chromatography, liquid chromatography, mass spectrometry, ELISA, antibody linkage, immunoassay, biochip assay, microarray, nanoassay, spectroscopy, a multiplex molecular assay or techniques which utilize a fluorescent, enzyme, radioactive, metallic, biotin, chemiluminescent, bioluminescent molecule assay. Suitable methods further include a combination of a detection techniques of nucleic acids and proteins or peptides. In some methods, at least one biomarker and/or glycobiomarker of Tables 5A, 5, 6A, 6 and 9 is immobilized on a solid support. In some methods, the testing is conducted by reacting the patient's sample with at least one anybody or protein chemistry based reagent specific to at least one biomarker and/or glycobiomarker of Tables 5A, 5, 6A, 6 and 9. In further embodiments, the testing is conducted by reacting the patient's sample with at least one protein specific to at least one biomarker and/or glycobiomarker of Tables 5A, 5, 6A, 6 and 9.
In some embodiments, the testing is conducted by reacting the patient's sample with a synthetic compound or probe which react with at least one protein specific to at least one biomarker and/or glycobiomarker of Tables 5A, 5, 6A, 6 and 9.
Further embodiments include a method for treating a disorder of the pancreas, the method comprising obtaining a sample from a mammal in need of the treatment and testing the sample for at least one or more biomarker and/or glycobiomarker selected from Tables 5A, 5, 6A, 6 and 9.
A method is provided for determining a state or probability of a pancreatic disorder, the method comprising: (a) determining the level of one or more biomarker and/or glycobiomarker selected from Tables 5A, 5, 6A, 6 and 9, and (b) the level of CA 19-9.
A method is provided for determining a state or probability of a pancreatic disorder, the method comprising: (a) determining the level of one or more biomarker and/or glycobiomarker selected from Tables 5A, 5, 6A, 6 and 9 and (b) the level of amylase.
A method is provided for determining a state or probability of a pancreatic disorder, the method comprising: (a) determining the level of one or more biomarker and/or glycobiomarker selected from Tables 5A, 5, 6A, 6 and 9, and (b) the level of glycosylated protein.
A method is provided for determining a state or probability of a pancreatic disorder, the method comprising: (a) determining the level of one or more biomarker and/or glycobiomarker selected from Tables 5A, 5, 6A, 6 and 9, and (b) the level of RNA or DNA.
A method is provided for determining a state or probability of a pancreatic disorder, the method comprising: (a) determining the level of one or more biomarker and/or glycobiomarker selected from Tables 5A, 5, 6A, 6 and 9, and (b) the level of virus or viral infection of the patient.
In some embodiments, a screening can be conducted with a patient's sample without protein extraction. In further embodiments, proteins are extracted from the patient's sample and a test is conducted with the extracted proteins. In some embodiments, all proteins in the sample are analyzed. In other embodiments, the analysis is conducted only for proteins which are abnormally glycosylated.
The advantages of these screening methods include: these tests are non-invasive and they can be conducted in a very short period of time. In some embodiments, the same test can be repeated several times within a period of time to monitor progression of a pancreatic disease and/or access the efficiency of a treatment plan.
Further embodiments provide Multiplex Molecular Diagnostic Protein assays, a combination of protein assay and assay based on detection of DNA or RNA, and kits, including an immunoassay, biochip assay, nanoassay and molecular assay. In some embodiments an assay detects protein biomarker and/or glycobiomarkers or peptides derived from the biomarker and/or glycobiomarkers from Tables 5A, 5, 6A, and 6. In other embodiments, an assay detects biomarkers or peptides derived from biomarkers from Table 9. In other embodiments, an assay detects biomarkers or peptides derived from biomarkers from Table 9. In other embodiments, an assay detects biomarkers or peptides derived from biomarkers from Table 9.
In further embodiments, a patient's sample is reacted with a set of antibodies, each of which is selectively specific for at least one biomarker and/or glycobiomarker from Tables 5A, 5, 6A, 6 and 9. The complex between an antibody and a glycobiomarker or a biomarker is then may be detected with a second antibody conjugated to a detection molecule.
Further embodiments include methods for detecting and monitoring a pancreatic disease, including pancreatic cancer, pancreatitis, and autoimmune pancreatitis. In these methods, a patient's sample is tested for expression of at least some biomarker and/or glycobiomarkers listed in Tables 5A, 5, 6A, 6 and 9. Further embodiments include methods in which patient's response to therapy is monitored with testing a patient's sample for expression of at least some biomarker and/or glycobiomarkers listed in Tables 5A, 5, 6A, 6 and 9. Other applications include detecting a recurrent or residual pancreatic disease by testing a patient's sample for expression of at least some biomarker and/or glycobiomarkers listed in Tables 5A, 5, 6A, 6 and 9.
Other applications include screening of genetically predisposed individuals for a pancreatic disease by testing the individual's sample for expression of at least some biomarkers and/or glycobiomarkers listed in Tables 5A, 5, 6A, 6 and 9. Such genetically predisposed individuals include, but not limited, to BRCA mutation carriers; PALB2 mutation carriers; p16 mutation carriers; Lynch syndrome patients; Peutz-Jeghers syndrome patients; and individuals with a family history of pancreatic cancer cases.
In some methods, a biochip comprising a set of at least one or more biomarker and/or glycobiomarkers listed in Tables 5A, 5, 6A, 6 and 9 can be used as a robust and sensitive tool to monitor disease progression and response to therapy. These biochips can be also used as a biomarker or molecular modality for drug development or drug optimization.
Other applications include tests conducted for detection and measurement of biomarker and/or glycobiomarkers which include at least one or more biomarkers listed in Tables 5A, 5, 6A, 6 and 9. Such tests may include verification and support of clinical and operative decision-making process and management of pancreatic cyst neoplasms.
Further embodiments provide methods for treating a disorder of the pancreas, the method comprising obtaining a sample from a mammal in need of the treatment and testing the sample for at least one or more biomarker and/or glycobiomarker selected from Tables 5A, 5, 6A, 6 and 9.
The invention will be now further explained by the following non-limiting examples.
Human plasma was obtained from cancer patients. Human plasma samples from healthy control individuals were used as controls. All patients and control individuals have provided informed consent, and collection of human samples was approved by the local Review Board. Glycosylated forms of proteins were isolated from human plasma as it has been described before (Khidekel N, Ficarro S B, Peters E C, Hsieh-Wilson L C. “Exploring the O-GlcNAc proteome: direct identification of O-GlcNAc-modified proteins from the brain”. PNAS 2004 Sep. 7; 101(36):13132-7. Yi W, Clark P M, Mason D E, Keenan M C, Hill C, Goddard W A 3rd, Peters E C, Driggers E M, Hsieh-Wilson L C. “Phosphofructokinase 1 glycosylation regulates cell growth and metabolism”. Science. 2012 Aug. 24; 337(6097):975-80.) Then isolated glycosylated forms of proteins from human plasma were used for the proteomics analysis.
Mass Spectrometry of protein expression profiling was performed according to the established protocol. Briefly, each isolated sample was processed by SDS-PAGE, using a 10% Bis-Tris NuPAGE gel (Invitrogen) with the MES buffer system. The entire mobility region was excised and processed by in-gel digestion using a robot (ProGest, DigiLab) with the following protocol: washed with 25 mM ammonium bicarbonate followed by acetonitrile; reduced with 10 mM dithiothreitol at 60° C. followed by alkylation with 50 mM iodoacetamide at RT. digested with trypsin (Promega) at 37° C. for 4 h, quenched with formic acid and the supernatant was analyzed directly without further processing. Mass Spectrometry of each digested sample was analyzed by nano LC-MS/MS with a ThermoFisher EASY-nLC 1000 HPLC system interfaced to a ThermoFisher Q Exactive mass spectrometer.
A sample was then loaded on a trapping column and eluted over a 75 μm×150 mm analytical column (Thermo Fisher P/N 164568) at 300 nL/min using a 4 hr reverse phase gradient; both columns were packed with Acclaim PepMap 100 Å, 3 3 μm resin (Thermo Scientific). The mass spectrometer was operated in the data-dependent mode, with MS and MS/MS performed in the Orbitrap at 70,000 and 17,500 FWHM resolution respectively. The fifteen most abundant ions were selected for MS/MS. The data processing was analyzed using a Mascot. Mascot DAT files were parsed into the Scaffold software for validation, filtering and to create a nonredundant list per sample. The data was filtered using at 1% protein and peptide FDR and requiring at least two unique peptides per protein.
The list of proteins was then additionally analyzed with the ELSEVIER PATHWAY STUDIO software.
Human plasma was obtained from cancer patients. Human plasma samples from healthy control individuals were used as controls. All patients and control individuals have provided informed consent, and collection of human samples was approved by the local Review Board. 10 μL of each plasma sample was processed using the Multiple Affinity Removal System (MARS specific for the 14 most abundant human plasma proteins (Agilent (P/N5188-6560)). The sample was processed using the vendor protocol. Depleted samples were buffer exchanged against HPLC grade water and quantified by Qubit fluorometry at a 1:10 dilution and % depletion was assessed. 20 μg of each sample was digested with trypsin using the following protocol: reduced with 10 mM dithiothreitol at 60° C. for 30 minutes in 25 mM Ammonium bicarbonate; alkylated with iodoacetamide for at 60° C. for 45 minutes in 25 mM Ammonium bicarbonate; digested overnight with sequencing grade trypsin at 37° C., enzyme: substrate ratio 1:20; quenched with formic acid. Digested samples were desalted using a Waters HLB μElution plate per the vendor protocol. Desalted samples were suspended in 100 μL of 0.1% TFA for analysis.
Mass Spectrometry of protein expression profiling was performed according to the established protocol. Briefly, each isolated sample was processed by SDS-PAGE, using a 10% Bis-Tris NuPAGE gel (Invitrogen) with the MES buffer system. The entire mobility region was excised and processed by in-gel digestion using a robot (ProGest, DigiLab) with the following protocol: washed with 25 mM ammonium bicarbonate followed by acetonitrile; reduced with 10 mM dithiothreitol at 60° C. followed by alkylation with 50 mM iodoacetamide at RT. digested with trypsin (Promega) at 37° C. for 4 h, quenched with formic acid and the supernatant was analyzed directly without further processing. Mass Spectrometry of each digested sample was analyzed by nano LC-MS/MS with a ThermoFisher EASY-nLC 1000 HPLC system interfaced to a ThermoFisher Q Exactive mass spectrometer.
A sample was then loaded on a trapping column and eluted over a 75 μm×150 mm analytical column (Thermo Fisher P/N 164568) at 300 nL/min using a 4 hr reverse phase gradient; both columns were packed with Acclaim PepMap 100 Å, 3 3 μm resin (Thermo Scientific). The mass spectrometer was operated in the data-dependent mode, with MS and MS/MS performed in the Orbitrap at 70,000 and 17,500 FWHM resolution respectively. The fifteen most abundant ions were selected for MS/MS. The data processing was analyzed using a Mascot. Mascot DAT files were parsed into the Scaffold software for validation, filtering and to create a nonredundant list per sample. The data was filtered using at 1% protein and peptide FDR and requiring at least two unique peptides per protein.
This application claims a benefit of priority to U.S. patent application Ser. No. 62/321,294, filed Apr. 12, 2016, the entire disclosure of which is incorporated herein by reference.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2017/027196 | 4/12/2017 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62321294 | Apr 2016 | US |
